Coupled Process/Device Modeling and Point Defect Engineering of Cu(In,Ga)Se<sub>2</sub> Solar Cells

Xiang, Xiaofeng; Sommer, David E.; Gehrke, Aaron; Dunham, Scott T.

doi:10.1109/jphotov.2024.3366652

Cited by 2 publications

(1 citation statement)

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“…Additionally, it is important to acknowledge that the “frozen-in” approximation might not hold in certain cases, particularly when the interactions between different defects are significant, like vacancy-interstitial pair annihilation and exchange reactions . Also, the strong Coulomb interaction between defects with opposite charges can give rise to defect complexes, , a consideration not addressed within the current framework.…”

Section: Methodsmentioning

confidence: 99%

Exploration of Deep Learning Models for Accelerated Defect Property Predictions and Device Design of Cubic Semiconductor Crystals

Xiang,

Soh,

Dunham

2024

J. Phys. Chem. C

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In this work, we present an exploration of deep learning models for predicting defect properties in cubic phase semiconductors. The nature of impurity energy levels strongly influences the performance of semiconductors in a wide range of applications, such as solar cells, field effect transistors, and qubits for quantum computing. In this work, we employ two types of deep learning models, a crystal defect graph neural network and a chemical environment-encoded artificial neural network, to predict defect properties. The models are trained on a data set of charge-dependent defect formation energies obtained from density functional theory computations and descriptors based on elemental properties, defect local environment, and relevant semiconductor properties. We assess the models' performance and showcase their capability in optimizing semiconductor devices, particularly when used in tandem with compositionally constrained thermodynamics and technology computer-aided design models.

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Section: Methodsmentioning

confidence: 99%

Exploration of Deep Learning Models for Accelerated Defect Property Predictions and Device Design of Cubic Semiconductor Crystals

Xiang,

Soh,

Dunham

2024

J. Phys. Chem. C

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Point defects in CdTe and CdTeSe alloy: A first principles investigation with DFT+U

Xiang,

Tong,

Gehrke

et al. 2024

Phys. Rev. Materials

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Coupled Process/Device Modeling and Point Defect Engineering of Cu(In,Ga)Se₂ Solar Cells

Cited by 2 publications

References 57 publications

Exploration of Deep Learning Models for Accelerated Defect Property Predictions and Device Design of Cubic Semiconductor Crystals

Exploration of Deep Learning Models for Accelerated Defect Property Predictions and Device Design of Cubic Semiconductor Crystals

Point defects in CdTe and CdTeSe alloy: A first principles investigation with DFT+U

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Coupled Process/Device Modeling and Point Defect Engineering of Cu(In,Ga)Se2 Solar Cells

Cited by 2 publications

References 57 publications

Exploration of Deep Learning Models for Accelerated Defect Property Predictions and Device Design of Cubic Semiconductor Crystals

Exploration of Deep Learning Models for Accelerated Defect Property Predictions and Device Design of Cubic Semiconductor Crystals

Point defects in CdTe and CdTeSe alloy: A first principles investigation with DFT+U

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Coupled Process/Device Modeling and Point Defect Engineering of Cu(In,Ga)Se₂ Solar Cells